﻿Allen HuangAssociate Professor, Department of Accounting, HKUST

Publications

"Imperfect Accounting and Reporting Bias?"(with Vivian Fang and Wenyu Wang), Journal of Accounting Research​, September 2017, Vol. 55, Iss. 4, pp. 919-962.Errors and bias are both inherent features of accounting. In theory, while errors discourage bias by lowering the value relevance of accounting, they can also facilitate bias by providing camouflage. Consistent with theory, we find a hump-shaped relation between a firm’s propensity to engage in intentional misstatement and the prevalence of unintentional misstatements in the firm’s industry for the whole economy and a majority of the industries. The result is robust to using firms’ number of items in financial statements and exposure to complex accounting rules as alternative proxies for errors and to using the restatement amount in net income to quantify the magnitude of bias and errors. To directly test for the two effects of errors, we show that, when errors are more prevalent, the market reacts less to firms’ earnings surprises and bias is more difficult to detect. Our results highlight the imperfectness of accounting, advance understanding of firms’ reporting incentives, and shed light on accounting standard setting.​ ​

"Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach"(with Reuven Lehavy, Amy Zang, and Rong Zheng), Management Science​, Forthcoming.This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply repeats managers’ discussion. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures. Internet Appendix to Huang, Lehavy, Zang and Zheng (2016).﻿﻿Java package to implement Topic Modeling (LDA) algorithm used in the paper.﻿ Another Java based package (MALLET).​Python package to implement LDA.

﻿"Short Selling and Earnings Management: A Controlled Experiment"﻿(with Vivian Fang and Jonathan Karpoff), Journal of Finance, June 2016, Vol. 71, Iss. 3, pp. 1251-1294. During 2005 to 2007, the SEC ordered a pilot program in which one-third of the Russell 3000 index were arbitrarily chosen as pilot stocks and exempted from short-sale price tests. Pilot firms’ discretionary accruals and likelihood of marginally beating earnings targets decrease during this period, and revert to pre-experiment levels when the program ends. After the program starts, pilot firms are more likely to be caught for fraud initiated before the program, and their stock returns better incorporate earnings information. These results indicate that short selling, or its prospect, curbs earnings management, helps detect fraud, and improves price efficiency. List of pilot stocks published SEC's release No. 50104.​

﻿﻿"Evidence on the Information Content of Text in Analyst Reports"(with Amy Zang and Rong Zheng), The Accounting Review, November 2014, Vol. 89, No. 6, pp. 2151-2180. We document that textual discussions in a sample of 363,952 analyst reports provide information to investors beyond that in the contemporaneously released earnings forecasts, stock recommendations, and target prices, and also assist investors in interpreting these signals. Cross-sectionally, we find that investors react more strongly to negative than to positive text, suggesting that analysts are especially important in propagating bad news. Additional evidence indicates that analyst report text is more useful when it places more emphasis on nonfinancial topics, is written more assertively and concisely, and when the perceived validity of other information signals in the same report is low. Finally, analyst report text is shown to have predictive value for future earnings growth in the subsequent five years.﻿Data mining software to implement the naive Bayes algorithm.﻿​

﻿﻿"CEO Reputation and Earnings Quality"﻿(with Jennifer Francis, Shivaram Rajgopal and Amy Zang), Contemporary Accounting Research, Spring 2008, Vol. 25, Iss. 1, pp. 109-147. We examine the relation between CEO reputation and measures of the firm’s earnings quality. Using press coverage (media counts) to proxy for CEO reputation, we find that more reputed CEOs are associated with poorer earnings quality. This finding is inconsistent with an efficient contracting view, which predicts that reputed CEOs take actions that result in good earnings quality. This seemingly counterintuitive result is, however, consistent with two other theories: a rent extraction hypothesis (which predicts that reputed managers are more likely to use their discretion to manipulate earnings in order to manage labor and stock market perceptions) and a matching hypothesis (which predicts that selection on the part of firms gives rise to a demand for reputed CEOs for firms where earnings quality is inherently poor). Further analyses provide little support for the rent extraction explanation and some support for the matching explanation.​

Selected Working Papers

"The Long-Term Consequences of Short-Term Incentives"(with Alex Edmans and Vivian Fang).Following the political theory of judicial decision-making, our paper proposes a parsimonious ex-ante litigation risk measure: federal court judge ideology. We find that judge ideology complements existing measures of litigation risks based on industry membership and firm characteristics, and provides incremental explanatory power in predicting litigation occurrence and outcomes. Firms in more liberal circuits are more likely to be sued. This effect is stronger for firms with more sophisticated leading plaintiffs and higher expected litigation payoffs, as well as for lawsuits without specific allegations. Next, we show that lawsuits filed in more liberal circuits are less likely to be dismissed and result in higher settlement amounts. Using judge departures as exogenous shocks to court ideology, we find that liberal changes increase the likelihood of lawsuit filings and that the market reacts negatively to the departures. Finally, using the new measure, we document that litigation risk deters managers from providing long-term earnings guidance, which existing measures cannot explain.

"Federal Judge Ideology: A New Measure of Ex-Ante Litigation Risk"(with Kai Wai Hui and Reeyarn Li).Following the political theory of judicial decision-making, our paper proposes a parsimonious ex-ante litigation risk measure: federal court judge ideology. We find that judge ideology complements existing measures of litigation risks based on industry membership and firm characteristics, and provides incremental explanatory power in predicting litigation occurrence and outcomes. Firms in more liberal circuits are more likely to be sued. This effect is stronger for firms with more sophisticated leading plaintiffs and higher expected litigation payoffs, as well as for lawsuits without specific allegations. Next, we show that lawsuits filed in more liberal circuits are less likely to be dismissed and result in higher settlement amounts. Using judge departures as exogenous shocks to court ideology, we find that liberal changes increase the likelihood of lawsuit filings and that the market reacts negatively to the departures. Finally, using the new measure, we document that litigation risk deters managers from providing long-term earnings guidance, which existing measures cannot explain.